Human tracking system

ABSTRACT

An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A background included in the grid of voxels may also be removed to isolate one or more voxels associated with a foreground object such as a human target. A location or position of one or more extremities of the isolated human target may be determined and a model may be adjusted based on the location or position of the one or more extremities.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/034,484 filed Sep. 23, 2013, which is a continuation of U.S. patentapplication Ser. No. 13/365,121 filed Feb. 2, 2012 (now U.S. Pat. No.8,542,910 issued Sep. 24, 2009), which is a continuation of U.S. patentapplication Ser. No. 12/575,388 filed on Oct. 7, 2009 (now U.S. Pat. No.8,564,534 issued Oct. 22, 2013), each of which is hereby incorporated byreference in its entirety.

BACKGROUND

Many computing applications such as computer games, multimediaapplications, or the like use controls to allow users to manipulate gamecharacters or other aspects of an application. Typically such controlsare input using, for example, controllers, remotes, keyboards, mice, orthe like. Unfortunately, such controls can be difficult to learn, thuscreating a barrier between a user and such games and applications.Furthermore, such controls may be different than actual game actions orother application actions for which the controls are used. For example,a game control that causes a game character to swing a baseball bat maynot correspond to an actual motion of swinging the baseball bat.

SUMMARY

Disclosed herein are systems and methods for tracking a user in a scene.For example, an image such as depth image of a scene may be received orobserved. A grid of voxels may then be generated based on the depthimage such that the depth image may be downsampled. For example, thedepth image may include a plurality of pixels that may be divided intoportions or blocks. A voxel may then be generated for each portion orblock such that the received depth image may be downsampled into thegrid of voxels.

According to one embodiment, a background included in the grid of voxelsmay then be removed to isolate one or more voxels associated with aforeground object such as a human target. A location or position of oneor more extremities such as a centroid or center, head, shoulders, hips,arms, hands, elbows, legs, feet, knees, or the like of the isolatedhuman target may be determined Additionally, dimensions such asmeasurements including widths, lengths, or the like of the extremitiesmay be determined

A model may then be adjusted based on the location or position of theone or more extremities and/or the dimensions determined therefore. Forexample, the model may be a skeletal model that may include jointsand/or bones. One or more of the joints of the model may be adjustedsuch that the one or more joints may be assigned to the location orposition of the one or more extremities corresponding thereto and/or thebones defined therebetween may be adjusted to the dimensions of the oneor more extremities corresponding thereto.

The adjusted model may be processed. For example, in one embodiment, theadjusted may be mapped to an avatar or game character such that theavatar or game character may be animated to mimic the user and/or theadjusted model may be provided to a gestures library in a computingenvironment that may be used to determine controls to perform within anapplication based on positions of various body parts in the model.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter. Furthermore,the claimed subject matter is not limited to implementations that solveany or all disadvantages noted in any part of this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B illustrate an example embodiment of a targetrecognition, analysis, and tracking system with a user playing a game.

FIG. 2 illustrates an example embodiment of a capture device that may beused in a target recognition, analysis, and tracking system.

FIG. 3 illustrates an example embodiment of a computing environment thatmay be used to interpret one or more gestures in a target recognition,analysis, and tracking system and/or animate an avatar or on-screencharacter displayed by a target recognition, analysis, and trackingsystem.

FIG. 4 illustrates another example embodiment of a computing environmentthat may be used to interpret one or more gestures in a targetrecognition, analysis, and tracking system and/or animate an avatar oron-screen character displayed by a target recognition, analysis, andtracking system.

FIG. 5 depicts a flow diagram of an example method for tracking a userin a scene.

FIG. 6 illustrates an example embodiment of a depth image that may becaptured or observed.

FIGS. 7A-7B illustrate an example embodiment of a portion of the depthimage being downsampled.

FIG. 8 illustrates an example embodiment of a centroid or center beingestimated for a human target.

FIG. 9 illustrates an example embodiment of a bounding box that may bedefined to determine a core volume.

FIG. 10 illustrates an example embodiment of a head cylinder and a torsocylinder that may be created to score a head candidate.

FIG. 11 illustrates an example embodiment of a head-to-center vectorbased on a head and a centroid or center of a human target.

FIG. 12 illustrates an example embodiment of a shoulders volume box anda hips volume box determined based on a head-to-center vector.

FIG. 13 illustrates an example embodiment of shoulders and hips that maybe calculated based on a shoulders volume box and a hips volume box.

FIG. 14 illustrates an example embodiment of a cylinder that mayrepresent the core volume.

FIGS. 15A-15C illustrate example embodiments of a hand being determinedbased on anchor points.

FIG. 16 illustrates an example embodiment of hands and feet that may becalculated based on arm and leg average positions and/or anchor points.

FIG. 17 illustrates an example embodiment a model that may be generated.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

FIGS. 1A and 1B illustrate an example embodiment of a configuration of atarget recognition, analysis, and tracking system 10 with a user 18playing a boxing game. In an example embodiment, the target recognition,analysis, and tracking system 10 may be used to recognize, analyze,and/or track a human target such as the user 18.

As shown in FIG. 1A, the target recognition, analysis, and trackingsystem 10 may include a computing environment 12. The computingenvironment 12 may be a computer, a gaming system or console, or thelike. According to an example embodiment, the computing environment 12may include hardware components and/or software components such that thecomputing environment 12 may be used to execute applications such asgaming applications, non-gaming applications, or the like. In oneembodiment, the computing environment 12 may include a processor such asa standardized processor, a specialized processor, a microprocessor, orthe like that may execute instructions including, for example,instructions for receiving a depth image; generating a grid of voxelsbased on the depth image; removing a background included in the grid ofvoxels to isolate one or more voxels associated with a human target;determining a location or position of one or more extremities of theisolated human target; adjusting a model based on the location orposition of the one or more extremities, or any other suitableinstruction, which will be described in more detail below.

As shown in FIG. 1A, the target recognition, analysis, and trackingsystem 10 may further include a capture device 20. The capture device 20may be, for example, a camera that may be used to visually monitor oneor more users, such as the user 18, such that gestures and/or movementsperformed by the one or more users may be captured, analyzed, andtracked to perform one or more controls or actions within an applicationand/or animate an avatar or on-screen character, as will be described inmore detail below.

According to one embodiment, the target recognition, analysis, andtracking system 10 may be connected to an audiovisual device 16 such asa television, a monitor, a high-definition television (HDTV), or thelike that may provide game or application visuals and/or audio to a usersuch as the user 18. For example, the computing environment 12 mayinclude a video adapter such as a graphics card and/or an audio adaptersuch as a sound card that may provide audiovisual signals associatedwith the game application, non-game application, or the like. Theaudiovisual device 16 may receive the audiovisual signals from thecomputing environment 12 and may then output the game or applicationvisuals and/or audio associated with the audiovisual signals to the user18. According to one embodiment, the audiovisual device 16 may beconnected to the computing environment 12 via, for example, an S-Videocable, a coaxial cable, an HDMI cable, a DVI cable, a VGA cable, or thelike.

As shown in FIGS. 1A and 1B, the target recognition, analysis, andtracking system 10 may be used to recognize, analyze, and/or track ahuman target such as the user 18. For example, the user 18 may betracked using the capture device 20 such that the gestures and/ormovements of user 18 may be captured to animate an avatar or on-screencharacter and/or may be interpreted as controls that may be used toaffect the application being executed by computer environment 12. Thus,according to one embodiment, the user 18 may move his or her body tocontrol the application and/or animate the avatar or on-screencharacter.

As shown in FIGS. 1A and 1B, in an example embodiment, the applicationexecuting on the computing environment 12 may be a boxing game that theuser 18 may be playing. For example, the computing environment 12 mayuse the audiovisual device 16 to provide a visual representation of aboxing opponent 38 to the user 18. The computing environment 12 may alsouse the audiovisual device 16 to provide a visual representation of aplayer avatar 40 that the user 18 may control with his or her movements.For example, as shown in FIG. 1B, the user 18 may throw a punch inphysical space to cause the player avatar 40 to throw a punch in gamespace. Thus, according to an example embodiment, the computerenvironment 12 and the capture device 20 of the target recognition,analysis, and tracking system 10 may be used to recognize and analyzethe punch of the user 18 in physical space such that the punch may beinterpreted as a game control of the player avatar 40 in game spaceand/or the motion of the punch may be used to animate the player avatar40 in game space.

Other movements by the user 18 may also be interpreted as other controlsor actions and/or used to animate the player avatar, such as controls tobob, weave, shuffle, block, jab, or throw a variety of different powerpunches. Furthermore, some movements may be interpreted as controls thatmay correspond to actions other than controlling the player avatar 40.For example, in one embodiment, the player may use movements to end,pause, or save a game, select a level, view high scores, communicatewith a friend, etc. According to another embodiment, the player may usemovements to select the game or other application from a main userinterface. Thus, in example embodiments, a full range of motion of theuser 18 may be available, used, and analyzed in any suitable manner tointeract with an application.

In example embodiments, the human target such as the user 18 may have anobject. In such embodiments, the user of an electronic game may beholding the object such that the motions of the player and the objectmay be used to adjust and/or control parameters of the game. Forexample, the motion of a player holding a racket may be tracked andutilized for controlling an on-screen racket in an electronic sportsgame. In another example embodiment, the motion of a player holding anobject may be tracked and utilized for controlling an on-screen weaponin an electronic combat game.

According to other example embodiments, the target recognition,analysis, and tracking system 10 may further be used to interpret targetmovements as operating system and/or application controls that areoutside the realm of games. For example, virtually any controllableaspect of an operating system and/or application may be controlled bymovements of the target such as the user 18.

FIG. 2 illustrates an example embodiment of the capture device 20 thatmay be used in the target recognition, analysis, and tracking system 10.According to an example embodiment, the capture device 20 may beconfigured to capture video with depth information including a depthimage that may include depth values via any suitable techniqueincluding, for example, time-of-flight, structured light, stereo image,or the like. According to one embodiment, the capture device 20 mayorganize the depth information into “Z layers,” or layers that may beperpendicular to a Z axis extending from the depth camera along its lineof sight.

As shown in FIG. 2, the capture device 20 may include an image cameracomponent 22. According to an example embodiment, the image cameracomponent 22 may be a depth camera that may capture the depth image of ascene. The depth image may include a two-dimensional (2-D) pixel area ofthe captured scene where each pixel in the 2-D pixel area may representa depth value such as a length or distance in, for example, centimeters,millimeters, or the like of an object in the captured scene from thecamera.

As shown in FIG. 2, according to an example embodiment, the image cameracomponent 22 may include an IR light component 24, a three-dimensional(3-D) camera 26, and an RGB camera 28 that may be used to capture thedepth image of a scene. For example, in time-of-flight analysis, the IRlight component 24 of the capture device 20 may emit an infrared lightonto the scene and may then use sensors (not shown) to detect thebackscattered light from the surface of one or more targets and objectsin the scene using, for example, the 3-D camera 26 and/or the RGB camera28. In some embodiments, pulsed infrared light may be used such that thetime between an outgoing light pulse and a corresponding incoming lightpulse may be measured and used to determine a physical distance from thecapture device 20 to a particular location on the targets or objects inthe scene. Additionally, in other example embodiments, the phase of theoutgoing light wave may be compared to the phase of the incoming lightwave to determine a phase shift. The phase shift may then be used todetermine a physical distance from the capture device to a particularlocation on the targets or objects.

According to another example embodiment, time-of-flight analysis may beused to indirectly determine a physical distance from the capture device20 to a particular location on the targets or objects by analyzing theintensity of the reflected beam of light over time via varioustechniques including, for example, shuttered light pulse imaging.

In another example embodiment, the capture device 20 may use astructured light to capture depth information. In such an analysis,patterned light (i.e., light displayed as a known pattern such as gridpattern or a stripe pattern) may be projected onto the scene via, forexample, the IR light component 24. Upon striking the surface of one ormore targets or objects in the scene, the pattern may become deformed inresponse. Such a deformation of the pattern may be captured by, forexample, the 3-D camera 26 and/or the RGB camera 28 and may then beanalyzed to determine a physical distance from the capture device to aparticular location on the targets or objects.

According to another embodiment, the capture device 20 may include twoor more physically separated cameras that may view a scene fromdifferent angles to obtain visual stereo data that may be resolved togenerate depth information.

The capture device 20 may further include a microphone 30. Themicrophone 30 may include a transducer or sensor that may receive andconvert sound into an electrical signal. According to one embodiment,the microphone 30 may be used to reduce feedback between the capturedevice 20 and the computing environment 12 in the target recognition,analysis, and tracking system 10. Additionally, the microphone 30 may beused to receive audio signals that may also be provided by the user tocontrol applications such as game applications, non-game applications,or the like that may be executed by the computing environment 12.

In an example embodiment, the capture device 20 may further include aprocessor 32 that may be in operative communication with the imagecamera component 22. The processor 32 may include a standardizedprocessor, a specialized processor, a microprocessor, or the like thatmay execute instructions including, for example, instructions forreceiving a depth image; generating a grid of voxels based on the depthimage; removing a background included in the grid of voxels to isolateone or more voxels associated with a human target; determining alocation or position of one or more extremities of the isolated humantarget; adjusting a model based on the location or position of the oneor more extremities, or any other suitable instruction, which will bedescribed in more detail below.

The capture device 20 may further include a memory component 34 that maystore the instructions that may be executed by the processor 32, imagesor frames of images captured by the 3-D camera or RGB camera, or anyother suitable information, images, or the like. According to an exampleembodiment, the memory component 34 may include random access memory(RAM), read only memory (ROM), cache, Flash memory, a hard disk, or anyother suitable storage component. As shown in FIG. 2, in one embodiment,the memory component 34 may be a separate component in communicationwith the image capture component 22 and the processor 32. According toanother embodiment, the memory component 34 may be integrated into theprocessor 32 and/or the image capture component 22.

As shown in FIG. 2, the capture device 20 may be in communication withthe computing environment 12 via a communication link 36. Thecommunication link 36 may be a wired connection including, for example,a USB connection, a Firewire connection, an Ethernet cable connection,or the like and/or a wireless connection such as a wireless 802.11b, g,a, or n connection. According to one embodiment, the computingenvironment 12 may provide a clock to the capture device 20 that may beused to determine when to capture, for example, a scene via thecommunication link 36.

Additionally, the capture device 20 may provide the depth informationand images captured by, for example, the 3-D camera 26 and/or the RGBcamera 28, and/or a skeletal model that may be generated by the capturedevice 20 to the computing environment 12 via the communication link 36.The computing environment 12 may then use the model, depth information,and captured images to, for example, control an application such as agame or word processor and/or animate an avatar or on-screen character.For example, as shown, in FIG. 2, the computing environment 12 mayinclude a gestures library 190. The gestures library 190 may include acollection of gesture filters, each comprising information concerning agesture that may be performed by the skeletal model (as the user moves).The data captured by the cameras 26, 28 and the capture device 20 in theform of the skeletal model and movements associated with it may becompared to the gesture filters in the gesture library 190 to identifywhen a user (as represented by the skeletal model) has performed one ormore gestures. Those gestures may be associated with various controls ofan application. Thus, the computing environment 12 may use the gestureslibrary 190 to interpret movements of the skeletal model and to controlan application based on the movements.

FIG. 3 illustrates an example embodiment of a computing environment thatmay be used to interpret one or more gestures in a target recognition,analysis, and tracking system and/or animate an avatar or on-screencharacter displayed by the target recognition, analysis, and trackingsystem. The computing environment such as the computing environment 12described above with respect to FIGS. 1A-2 may be a multimedia console100, such as a gaming console. As shown in FIG. 3, the multimediaconsole 100 has a central processing unit (CPU) 101 having a level 1cache 102, a level 2 cache 104, and a flash ROM (Read Only Memory) 106.The level 1 cache 102 and a level 2 cache 104 temporarily store data andhence reduce the number of memory access cycles, thereby improvingprocessing speed and throughput. The CPU 101 may be provided having morethan one core, and thus, additional level 1 and level 2 caches 102 and104. The flash ROM 106 may store executable code that is loaded duringan initial phase of a boot process when the multimedia console 100 ispowered ON.

A graphics processing unit (GPU) 108 and a video encoder/video codec(coder/decoder) 114 form a video processing pipeline for high speed andhigh resolution graphics processing. Data is carried from the graphicsprocessing unit 108 to the video encoder/video codec 114 via a bus. Thevideo processing pipeline outputs data to an AN (audio/video) port 140for transmission to a television or other display. A memory controller110 is connected to the GPU 108 to facilitate processor access tovarious types of memory 112, such as, but not limited to, a RAM (RandomAccess Memory).

The multimedia console 100 includes an I/O controller 120, a systemmanagement controller 122, an audio processing unit 123, a networkinterface controller 124, a first USB host controller 126, a second USBcontroller 128 and a front panel I/O subassembly 130 that are preferablyimplemented on a module 118. The USB controllers 126 and 128 serve ashosts for peripheral controllers 142(1)-142(2), a wireless adapter 148,and an external memory device 146 (e.g., flash memory, external CD/DVDROM drive, removable media, etc.). The network interface 124 and/orwireless adapter 148 provide access to a network (e.g., the Internet,home network, etc.) and may be any of a wide variety of various wired orwireless adapter components including an Ethernet card, a modem, aBluetooth module, a cable modem, and the like.

System memory 143 is provided to store application data that is loadedduring the boot process. A media drive 144 is provided and may comprisea DVD/CD drive, hard drive, or other removable media drive, etc. Themedia drive 144 may be internal or external to the multimedia console100. Application data may be accessed via the media drive 144 forexecution, playback, etc. by the multimedia console 100. The media drive144 is connected to the I/O controller 120 via a bus, such as a SerialATA bus or other high speed connection (e.g., IEEE 1394).

The system management controller 122 provides a variety of servicefunctions related to assuring availability of the multimedia console100. The audio processing unit 123 and an audio codec 132 form acorresponding audio processing pipeline with high fidelity and stereoprocessing. Audio data is carried between the audio processing unit 123and the audio codec 132 via a communication link. The audio processingpipeline outputs data to the A/V port 140 for reproduction by anexternal audio player or device having audio capabilities.

The front panel I/O subassembly 130 supports the functionality of thepower button 150 and the eject button 152, as well as any LEDs (lightemitting diodes) or other indicators exposed on the outer surface of themultimedia console 100. A system power supply module 136 provides powerto the components of the multimedia console 100. A fan 138 cools thecircuitry within the multimedia console 100.

The CPU 101, GPU 108, memory controller 110, and various othercomponents within the multimedia console 100 are interconnected via oneor more buses, including serial and parallel buses, a memory bus, aperipheral bus, and a processor or local bus using any of a variety ofbus architectures. By way of example, such architectures can include aPeripheral Component Interconnects (PCI) bus, PCI-Express bus, etc.

When the multimedia console 100 is powered ON, application data may beloaded from the system memory 143 into memory 112 and/or caches 102, 104and executed on the CPU 101. The application may present a graphicaluser interface that provides a consistent user experience whennavigating to different media types available on the multimedia console100. In operation, applications and/or other media contained within themedia drive 144 may be launched or played from the media drive 144 toprovide additional functionalities to the multimedia console 100.

The multimedia console 100 may be operated as a standalone system bysimply connecting the system to a television or other display. In thisstandalone mode, the multimedia console 100 allows one or more users tointeract with the system, watch movies, or listen to music. However,with the integration of broadband connectivity made available throughthe network interface 124 or the wireless adapter 148, the multimediaconsole 100 may further be operated as a participant in a larger networkcommunity.

When the multimedia console 100 is powered ON, a set amount of hardwareresources are reserved for system use by the multimedia consoleoperating system. These resources may include a reservation of memory(e.g., 16 MB), CPU and GPU cycles (e.g., 5%), networking bandwidth(e.g., 8 kbs), etc. Because these resources are reserved at system boottime, the reserved resources do not exist from the application's view.

In particular, the memory reservation preferably is large enough tocontain the launch kernel, concurrent system applications and drivers.The CPU reservation is preferably constant such that if the reserved CPUusage is not used by the system applications, an idle thread willconsume any unused cycles.

With regard to the GPU reservation, lightweight messages generated bythe system applications (e.g., popups) are displayed by using a GPUinterrupt to schedule code to render popup into an overlay. The amountof memory required for an overlay depends on the overlay area size andthe overlay preferably scales with screen resolution. Where a full userinterface is used by the concurrent system application, it is preferableto use a resolution independent of application resolution. A scaler maybe used to set this resolution such that the need to change frequencyand cause a TV resynch is eliminated.

After the multimedia console 100 boots and system resources arereserved, concurrent system applications execute to provide systemfunctionalities. The system functionalities are encapsulated in a set ofsystem applications that execute within the reserved system resourcesdescribed above. The operating system kernel identifies threads that aresystem application threads versus gaming application threads. The systemapplications are preferably scheduled to run on the CPU 101 atpredetermined times and intervals in order to provide a consistentsystem resource view to the application. The scheduling is to minimizecache disruption for the gaming application running on the console.

When a concurrent system application requires audio, audio processing isscheduled asynchronously to the gaming application due to timesensitivity. A multimedia console application manager (described below)controls the gaming application audio level (e.g., mute, attenuate) whensystem applications are active.

Input devices (e.g., controllers 142(1) and 142(2)) are shared by gamingapplications and system applications. The input devices are not reservedresources, but are to be switched between system applications and thegaming application such that each will have a focus of the device. Theapplication manager preferably controls the switching of input stream,without knowledge the gaming application's knowledge and a drivermaintains state information regarding focus switches. The cameras 26, 28and capture device 20 may define additional input devices for theconsole 100.

FIG. 4 illustrates another example embodiment of a computing environment220 that may be the computing environment 12 shown in FIGS. 1A-2 used tointerpret one or more gestures in a target recognition, analysis, andtracking system and/or animate an avatar or on-screen characterdisplayed by a target recognition, analysis, and tracking system. Thecomputing system environment 220 is only one example of a suitablecomputing environment and is not intended to suggest any limitation asto the scope of use or functionality of the presently disclosed subjectmatter. Neither should the computing environment 220 be interpreted ashaving any dependency or requirement relating to any one or combinationof components illustrated in the exemplary operating environment 220. Insome embodiments the various depicted computing elements may includecircuitry configured to instantiate specific aspects of the presentdisclosure. For example, the term circuitry used in the disclosure caninclude specialized hardware components configured to performfunction(s) by firmware or switches. In other examples embodiments theterm circuitry can include a general purpose processing unit, memory,etc., configured by software instructions that embody logic operable toperform function(s). In example embodiments where circuitry includes acombination of hardware and software, an implementer may write sourcecode embodying logic and the source code can be compiled into machinereadable code that can be processed by the general purpose processingunit. Since one skilled in the art can appreciate that the state of theart has evolved to a point where there is little difference betweenhardware, software, or a combination of hardware/software, the selectionof hardware versus software to effectuate specific functions is a designchoice left to an implementer. More specifically, one of skill in theart can appreciate that a software process can be transformed into anequivalent hardware structure, and a hardware structure can itself betransformed into an equivalent software process. Thus, the selection ofa hardware implementation versus a software implementation is one ofdesign choice and left to the implementer.

In FIG. 4, the computing environment 220 comprises a computer 241, whichtypically includes a variety of computer readable media. Computerreadable media can be any available media that can be accessed bycomputer 241 and includes both volatile and nonvolatile media, removableand non-removable media. The system memory 222 includes computer storagemedia in the form of volatile and/or nonvolatile memory such as readonly memory (ROM) 223 and random access memory (RAM) 260. A basicinput/output system 224 (BIOS), containing the basic routines that helpto transfer information between elements within computer 241, such asduring start-up, is typically stored in ROM 223. RAM 260 typicallycontains data and/or program modules that are immediately accessible toand/or presently being operated on by processing unit 259. By way ofexample, and not limitation, FIG. 4 illustrates operating system 225,application programs 226, other program modules 227, and program data228.

The computer 241 may also include other removable/non-removable,volatile/nonvolatile computer storage media. By way of example only,FIG. 4 illustrates a hard disk drive 238 that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive 239that reads from or writes to a removable, nonvolatile magnetic disk 254,and an optical disk drive 240 that reads from or writes to a removable,nonvolatile optical disk 253 such as a CD ROM or other optical media.Other removable/non-removable, volatile/nonvolatile computer storagemedia that can be used in the exemplary operating environment include,but are not limited to, magnetic tape cassettes, flash memory cards,digital versatile disks, digital video tape, solid state RAM, solidstate ROM, and the like. The hard disk drive 238 is typically connectedto the system bus 221 through an non-removable memory interface such asinterface 234, and magnetic disk drive 239 and optical disk drive 240are typically connected to the system bus 221 by a removable memoryinterface, such as interface 235.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 4, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 241. In FIG. 4, for example, hard disk drive 238 is illustratedas storing operating system 258, application programs 257, other programmodules 256, and program data 255. Note that these components can eitherbe the same as or different from operating system 225, applicationprograms 226, other program modules 227, and program data 228. Operatingsystem 258, application programs 257, other program modules 256, andprogram data 255 are given different numbers here to illustrate that, ata minimum, they are different copies. A user may enter commands andinformation into the computer 241 through input devices such as akeyboard 251 and pointing device 252, commonly referred to as a mouse,trackball or touch pad. Other input devices (not shown) may include amicrophone, joystick, game pad, satellite dish, scanner, or the like.These and other input devices are often connected to the processing unit259 through a user input interface 236 that is coupled to the systembus, but may be connected by other interface and bus structures, such asa parallel port, game port or a universal serial bus (USB). The cameras26, 28 and capture device 20 may define additional input devices for theconsole 100. A monitor 242 or other type of display device is alsoconnected to the system bus 221 via an interface, such as a videointerface 232. In addition to the monitor, computers may also includeother peripheral output devices such as speakers 244 and printer 243,which may be connected through a output peripheral interface 233.

The computer 241 may operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computer246. The remote computer 246 may be a personal computer, a server, arouter, a network PC, a peer device or other common network node, andtypically includes many or all of the elements described above relativeto the computer 241, although only a memory storage device 247 has beenillustrated in FIG. 4. The logical connections depicted in FIG. 2include a local area network (LAN) 245 and a wide area network (WAN)249, but may also include other networks. Such networking environmentsare commonplace in offices, enterprise-wide computer networks, intranetsand the Internet.

When used in a LAN networking environment, the computer 241 is connectedto the LAN 245 through a network interface or adapter 237. When used ina WAN networking environment, the computer 241 typically includes amodem 250 or other means for establishing communications over the WAN249, such as the Internet. The modem 250, which may be internal orexternal, may be connected to the system bus 221 via the user inputinterface 236, or other appropriate mechanism. In a networkedenvironment, program modules depicted relative to the computer 241, orportions thereof, may be stored in the remote memory storage device. Byway of example, and not limitation, FIG. 4 illustrates remoteapplication programs 248 as residing on memory device 247. It will beappreciated that the network connections shown are exemplary and othermeans of establishing a communications link between the computers may beused.

FIG. 5 depicts a flow diagram of an example method 300 for tracking auser in a scene. The example method 300 may be implemented using, forexample, the capture device 20 and/or the computing environment 12 ofthe target recognition, analysis, and tracking system 10 described withrespect to FIGS. 1A-4. In an example embodiment, the example method 300may take the form of program code (i.e., instructions) that may beexecuted by, for example, the capture device 20 and/or the computingenvironment 12 of the target recognition, analysis, and tracking system10 described with respect to FIGS. 1A-4.

According to one embodiment, at 305, a depth image may be received. Forexample, the target recognition, analysis, and tracking system mayinclude a capture device such as the capture device 20 described abovewith respect to FIGS. 1A-2. The capture device may capture or observe ascene that may include one or more targets. In an example embodiment,the capture device may be a depth camera configured to obtain an imagesuch as a depth image of the scene using any suitable technique such astime-of-flight analysis, structured light analysis, stereo visionanalysis, or the like.

The depth image may be a plurality of observed pixels where eachobserved pixel has an observed depth value. For example, the depth imagemay include a two-dimensional (2-D) pixel area of the captured scenewhere each pixel in the 2-D pixel area may have a depth value such as alength or distance in, for example, centimeters, millimeters, or thelike of an object in the captured scene from the capture device.

FIG. 6 illustrates an example embodiment of a depth image 400 that maybe received at 305. According to an example embodiment, the depth image400 may be an image or frame of a scene captured by, for example, the3-D camera 26 and/or the RGB camera 28 of the capture device 20described above with respect to FIG. 2. As shown in FIG. 6, the depthimage 400 may include a human target 402 a corresponding to, forexample, a user such as the user 18 described above with respect toFIGS. 1A and 1B and one or more non-human targets 404 such as a wall, atable, a monitor, or the like in the captured scene. As described above,the depth image 400 may include a plurality of observed pixels whereeach observed pixel has an observed depth value associated therewith.For example, the depth image 400 may include a two-dimensional (2-D)pixel area of the captured scene where each pixel at a particularX-value and Y-value in the 2-D pixel area may have a depth value such asa length or distance in, for example, centimeters, millimeters, or thelike of a target or object in the captured scene from the capturedevice.

In one embodiment, the depth image 400 may be colorized such thatdifferent colors of the pixels of the depth image correspond to and/orvisually depict different distances of the human target 402 a andnon-human targets 404 from the capture device. For example, the pixelsassociated with a target closest to the capture device may be coloredwith shades of red and/or orange in the depth image whereas the pixelsassociated with a target further away may be colored with shades ofgreen and/or blue in the depth image.

Referring back to FIG. 5, in one embodiment, upon receiving the image,at 305, one or more high-variance and/or noisy depth values may beremoved and/or smoothed from the depth image; portions of missing and/orremoved depth information may be filled in and/or reconstructed; and/orany other suitable processing may be performed on the received depthimage may such that the depth information associated with the depthimage may used to generate a model such as a skeletal model, which willbe described in more detail below.

According to an example embodiment, at 310, a grid of one or more voxelsmay be generated based on the received depth image. For example, thetarget recognition, analysis, and tracking system may downsample thereceived depth image by generating one or more voxels using informationincluded in the received depth image such that a downsampled depth imagemay be generated. In one embodiment, the one or more voxels may bevolume elements that may represent data or values of the informationincluded in the received depth image on a sub-sampled grid.

For example, as described above, the depth image may include a 2-D pixelarea of the captured scene where each pixel may have an X-value, aY-value, and a depth value (or Z-value) associated therewith. In oneembodiment, the depth image may be downsampled by reducing the pixels inthe 2-D pixel area into a grid of one or more voxels. For example, thedepth image may be divided into portions or blocks of pixels such as 4×4blocks of pixels, 5×5 blocks of pixels, 8×8 block of pixels, a 10×10block of pixels, or the like. Each portion or block may be processed togenerate a voxel for the depth image that may represent a position ofthe portion or block associated the pixels of the 2-D depth image inreal-world space. According to an example embodiment, the position ofeach voxel may be generated based on, for example, an average depthvalue of the valid or non-zero depth values for the pixels in the blockor portion that the voxel may represent, a minimum, maximum, and/or amedian depth value of the pixels in the portion or block that the voxelmay represent, an average of the X-values and Y-values for pixels havinga valid depth value in the portion or the block that the voxel mayrepresent, or any other suitable information provided by the depthimage. Thus, according to an example embodiment, each voxel mayrepresent a sub-volume portion or block of the depth image having valuessuch as an average depth value of the valid or non-zero depth values forthe pixels in the block or portion that the voxel may represent, aminimum, maximum, and/or a median depth value of the pixels in theportion or block that the voxel may represent, an average of theX-values and Y-values for pixels having a valid depth value in theportion or the block that the voxel may represent, or any other suitableinformation provided by the depth image based on the X-values, Y-values,and depth values of the corresponding portion or block of pixels of thedepth image received at 305.

In one embodiment, the grid of the one or more voxels in the downsampleddepth image may be layered. For example, the target recognition,analysis, and tracking system may generate voxels as described above.The target recognition, analysis, and tracking system may then stack agenerated voxel over one or more other generated voxels in the grid.

Accoding to an example embodiment, the target recognition, analysis, andtracking system may stack voxels in the grid around, for example, edgesof objects in the scene that may be captured in the depth image. Forexample, a depth image received at 305 may include a human target and anon-human target such as a wall. The human target may overlap thenon-human target such as the wall at, for example, an an edge of thehuman target. In one embodiment, the overlapping edge may includeinformation such as depth values, X-values, Y-values, or the likeassociated with the human target and the non-human target that may becaptured in the depth image. The target recognition, analyisis, andtracking system may generate a voxel associated with the human targetand a voxel associated with the non-human target at the overlapping edgesuch that the voxels may be stacked and the information such as depthvalues, X-values, Y-values, or the like of the overlapping edge may beretained in the grid.

According to another embodiment, the grid of one or more voxels may begenerated at 310 by projecting, for example, information such as thedepth values, X-values, Y-values, or the like for the pixels in thedepth image that may be received at 305 into a three-dimensional (3-D)space. For example, the target recognition, analysis, and trackingsystem may map information such as the depth values, X-values, Y-values,or the like for the pixels in the depth image to 3-D points in the 3-Dspace using a transformation such as a camera, image, or perspectivetransform such that the information may be transformed as trapezoidal orpyramidal shapes in the 3-D space. In one embodiment, the 3-D spacehaving the trapezoidal or pyramidal shapes may be divided into blockssuch as cubes that may create a grid of voxels such that each of theblocks or cubes may represent a voxel in the grid. For example, thetarget recognition, analysis, and tracking system may superimpose a 3-Dgrid over the 3-D points that correspond to the object in the depthimage. The target recognition, analysis, and tracking system may thendivide or chop up the grid into the blocks representing voxels todownsample the depth image into a lower resolution. According to anexample embodiment, each of the voxels in the grid may include anaverage depth value of the valid or non-zero depth values for the pixelsassociated with the 3-D space in the grid that the voxel may represent,a minimum and/or maximum depth value of the pixels associated with the3-D space in the grid that the voxel may represent, an average of theX-values and Y-values for pixels having a valid depth value associatedwith the 3-D space in the grid that the voxel may represent, or anyother suitable information provided by the depth image.

FIGS. 7A-7B illustrate an example embodiment of a portion of the depthimage being downsampled. For example, as shown in FIG. 7A, a portion 410of the depth image 400 described above with respect to FIG. 6 mayinclude a plurality of pixels 420 where each pixel 420 may have anX-value, a Y-value, and a depth value (or Z-value) associated therewith.According to one embodiment, as described above, a depth image such asthe depth image 400 may be downsampled by reducing the pixels in the 2-Dpixel area into a grid of one or more voxels. For example, as shown inFIGS. 7A, the portion 410 of the depth image 400 may be divided into aportion or a block 430 of the pixels 420 such as 8x8 block of the pixels420. The target recognition, analysis, and tracking system may processthe portion or block 430 to generate a voxel 440 that may represent aposition of the portion or block 430 associated the pixels 420 inreal-world space as shown in FIGS. 7A-7B.

Referring back to FIG. 5, at 315, the background may be removed from thedownsampled depth image. For example, a background such as the non-humantargets or objects in the downsampled depth image may be removed toisolate foreground objects such as a human target associated with auser. In one embodiment, as described above, the target recognition,analysis, and tracking system may downsample a captured or observeddepth image by generating a grid of one or more voxels for the capturedor observed depth image. The target recognition, analysis, and trackingsystem may analyze each of the voxels in the downsampled depth image todetermine whether a voxel may be associated with a background objectsuch as one or more non-human targets of the depth image. If a voxel maybe associated with a background object, the voxel may be removed ordiscarded from the downsampled depth image such that a foreground objectsuch as the human target and the one or more voxels in the gridassociated with the foreground object may be isolated.

According to one embodiment, the target recognition, analysis, andtracking system may analyze each voxel to determine an object associatedtherewith. For example, as described above, a scene that may be observedor captured at 305 as a depth image such as the depth image 400described above with respect to FIG. 6 may include a plurality ofobjects. The objects may include one or more human targets and/or one ormore non-human targets such as a wall, a table, a couch, a lamp, or thelike. In one embodiment, the target, recognition, analysis, and trackingsystem may analyze each voxel in the grid to determine which object inthe scene the voxel may be as associated with such that the targetrecognition, analysis, and tracking system may identify voxelsassociated with each object in a scene at 315. Thus, according to anexample embodiment, if a human target or person may be standing in frontof a wall in a scene, the target recognition, analysis, and trackingsystem may analyze each voxel to determine whether the voxel may beassociated with the human target or the wall.

To determine which object in the scene a voxel may be associated with,the target, recognition, analysis, and tracking system may comparevalues such as an average depth value of the valid or non-zero depthvalues for the pixels in the block or portion that the voxel mayrepresent, a minimum and/or maximum depth value of the pixels in theportion or block that the voxel may represent, an average of the Xvalues and Y values for pixels having a valid depth value that the voxelmay represent, or any other suitable information of neighboring ornearby voxels. For example, in one embodiment, the average depth valueassociated with a particular voxel being analyzed in the grid may becompared to the average depth values of each voxel that may be adjacentto the particular voxel being analyzed in the grid. If the differencebetween the average depth value of the particular voxel being analyzedand an average depth value of an adjacent voxel may be less than athreshold, the particular voxel and the adjacent voxel may be identifiedas belonging to the same object. If the difference between the averagedepth value of the particular voxel being analyzed and an average depthvalue of an adjacent voxel may be greater than the threshold, theparticular voxel and the adjacent voxel may be identified as belongingto separate objects. According to an example embodiment, the thresholdmay be a predetermined value generated by, for example, the targetrecognition, analysis, and tracking system that may be based on alikelihood or probability that voxels may be part of the same object.Thus, according to an example embodiment, if a human target or personmay be standing in front of a wall in a scene captured or observed bythe depth image, the target recognition, analysis, and tracking systemmay analyze each voxel generated for the depth image to determinewhether the voxel may be associated with the human target or the wall.

After identifying the objects and the voxels associated therewith in thescene of the received depth image, the target recognition, analysis, andtracking system may then calculate information associated with eachidentified object. For example, the target recognition, analysis, andtracking system may calculate a maximum world space for each identifiedobject, a minimum world space position, and an average world spaceposition, or the like.

In one embodiment, the target recognition, analysis, and tracking systemmay further determine whether one or more of the objects identified in ascene should be merged with other objects in the scene at 315. Forexample, part or a portion of an object may be separated from anotherpart or portion of the object in the depth image received at 305.According to one embodiment, the part or portion of an object may beseparated from another part or portion of the object by an infraredshadow that may be cast by, for example, the object, another object, orthe like in the scene. In another example embodiment, the part orportion of an object may be separated from another part or portion ofthe object by, for example, colors, textures, patterns, or the likeassociated with the object. For example, a head of a human target may beseparated from a torso of the human target along a Y-plane in theY-direction by, for example, facial hair, various articles of clothing,or the like.

To determine whether an object identified in the scene may actually be apart or a portion of another object identified in the scene, the targetrecognition, analysis, and tracking system may compare the X-values andthe depth values of the voxels associated with the object with X-valuesand depth values of the voxels associated with nearby objects. Forexample, the target recognition, analysis, and tracking system maycompare an X-value and a depth value of one or more voxels associatedwith, for example, a first object identified in the scene with anX-value and a depth value of one or more voxels associated with a secondobject that may be nearby or adjacent to the first object. Thus,according to an example embodiment, the target recognition, analysis,and tracking system may analyze the voxels in a scene to determinewhether a first and second object may overlap along the X-plane definedin the X-direction and/or the Z-plane defined in the Z-direction suchthat the first and second objects may be merged and identified as beingparts or portions of the same object.

According to one embodiment, if the X-value and the depth value of oneor more voxels associated with the first object may overlap an X-valueand a depth value of one or more voxels associated with the secondobject, the target recognition, analysis, and tracking system may mergethe first and second objects such that the target recognition, analysis,and tracking system may identify the first and second objects as beingparts or portions of a common object. For example, if the first voxelassociated with the first object may have an X-value of 5 along theX-direction and a depth value of 10 mm at a right outer edge of thefirst object and the second voxel associated with the second object mayhave an X-value of 3 along the X-direction and a depth value of 10 mm ata left outer edge of the second object, the target recognition,analysis, and target system may determine that the first and secondobjects may overlap. The target, recognition, analysis, and trackingsystem may then merge the first and second objects such that the target,recognition, analysis, and tracking system may identify the first andsecond objects as being parts or portions of the same object.

Additionally, to determine whether an object identified in the scene mayactually be a part or a portion of another object identified in thescene, the target recognition, analysis, and tracking system maydetermine whether a bounding box defined for an object overlaps abounding box of another object in the scene. For example, the targetrecognition, analysis, and tracking system may define a bounding box foreach identified object. The target recognition, analysis, and trackingsystem may then determine whether the bounding boxes of one or moreobjects overlap based on, for example, X-values, Y-values, and/or depthvalues of one or more voxels included therein as described above.

According to another example, embodiment, the target recognition,analysis, and tracking system may determine a center or centroid of eachobject by, for example, averaging the X-values, Y-values, and depthvalues of the voxels included in the object. The target recognition,analysis, and tracking system may then determine a distance between thecentroid or center of objects in the scene to determine whether anobject identified in the scene may actually be a part or a portion ofanother object identified in the scene. Based on the distance betweenobjects, the target, recognition, analysis, and tracking system maymerge one or more objects. For example, the target recognition,analysis, and tracking system may determine a distance between acentroid or center of a first object and a center or centroid of asecond object. If the distance between the centroid or center of thefirst object and the second object may be within a predetermined rangethat indicates the first and second objects should be merged, the targetrecognition, analysis, and tracking system may merge the objects suchthat the target, recognition, analysis, and tracking system may identifythe first and second objects as being parts or portions of the sameobject.

In one embodiment, the target recognition, analysis, and tracking systemmay further determine whether one or more of the objects identified inthe scene should be separated at 315. For example, an object identifiedin the scene at 315 may actually be two separate objects. To determinewhether an object in the scene should be separated, the targetrecognition, analysis, and tracking system may identify a location of acenter of each object determined for a previously received frame.According to one embodiment, the target recognition, analysis, andtracking system may then simultaneously floodfill the voxels in thescene generated for the depth image of the frame received at 305starting with the location of the center determined from the objects ofthe previously received frame. The target recognition, analysis, andtracking system may then determine which object in the previouslyreceived frame the floodfilled voxels may be closer to using theprevious locations for the objects. The target recognition, analysis,and tracking system may split an object at 315 if the floodfilled voxelsmay be closer to another object identified in a previously receivedframe.

At 315, the target recognition, analysis, and tracking system may thendetermine whether the identified objects may be a background object suchas non-human target or a foreground object such as a human target.According to an example embodiment, the target recognition, analysis,and tracking system may determine whether the identified objects may bea background object or a foreground object based on whether theidentified objects may be in motion or moving. For example, the targetrecognition, analysis, and tracking system may include a reference platesuch as a reference image of the scene that includes, for example,non-motion depth information for each voxel. According to exampleembodiments, the reference plate may include a minimum world spaceposition of the voxels such as the minimum X-values, Y-values, and depthvalues for the voxels in the grid determined over a series of frames, amaximum world space position of the voxels such as the maximum X-values,Y-values, and depth values for the voxels in the grid determined over aseries of frames, an average world position of the voxels such as theaverage X-values, Y-values, and depth values for the voxels in the griddetermined of a series of frames, or any other suitable reference plate.In another embodiment, the reference plate may include a moving averageassociated with each voxel in the scene. The moving average may include,for example, an average depth value of a voxel determined over a seriesof previously received frames.

According to one embodiment, the target recognition, analysis, andtracking system may compare depth information such as a maximum depthvalue, an average depth value, a minimum depth value, or the like ofeach voxel associated with the identified objects in the scene of thedepth image received at, for example, 305 with the non-motion depthinformation of each corresponding voxel included in the reference plate.Based on the comparison of the depth information and the non-motiondepth information of the corresponding voxel in the reference plate, thetarget recognition, analysis, and tracking system may identify a voxelas moving. For example, in one embodiment, if a depth value such as theminimum depth value, the maximum depth value, and/or the average depthvalue of a voxel may be less than the moving average of thecorresponding voxel in the reference plate such that the voxel may be infront of the moving average, the voxel may be identified as moving.According to another example embodiment, the target recognition,analysis, and tracking system may calculate a difference between thevalues associated with the voxel and the corresponding voxel in thereference plate. If, for example, a difference between a depth valuesuch as the average depth value, the maximum depth value, and/or theminimum depth value of a voxel and depth values included in thenon-motion information of the corresponding voxel in the reference platemay be greater than a motion threshold, the voxel may be identified bythe target recognition, analysis, and tracking system as moving.

In another example embodiment, the target recognition, analysis, andtracking system may compare depth information such as a maximum depthvalue, an average depth value, a minimum depth value, or the like of avoxel and the voxels adjacent thereto with the non-motion depthinformation of each corresponding voxel included in the reference plate.For example, to handle edge noise, the target recognition, analysis, andtracking system may compare a minimum depth value of a particular voxeland the voxels that may be adjacent thereto against the correspondingvoxel in the reference plate to determine whether a voxel and/or theobject associated therewith may be moving. If, for example, a differencebetween the minimum depth value of the particular voxel and the voxelsthat may be adjacent thereto and the minimum depth value included in thenon-motion information of the corresponding voxels in the referenceplate may be greater than a motion threshold, the particular voxel maybe identified by the target recognition, analysis, and tracking systemas moving.

The target recognition, analysis, and tracking system may then calculatea foreground score for each identified object based on a percentage ofmoving voxels. In one embodiment, the target recognition, analysis, andtracking system may divide the number of voxels included in the islandthat may be identified as moving by the total number of voxels includedin the island to calculate the foreground score.

The target recognition, analysis, and tracking system may then isolatethe object having a foreground score that may exceed a score threshold.The score threshold that may be a value or percentage defined by thetarget recognition, analysis, and tracking system that may indicate anobject may be in motion. For example, the target recognition, analysis,and tracking system may remove or discard the background objects thatmay not be moving based on the foreground score from the downsampleddepth image such that the foreground object such as the human targetthat may have a foreground score that may exceed the score threshold maybe isolated in the downsampled depth image. According to an exampleembodiment, to remove or discard the objects that may not be moving, thetarget recognition, analysis, and tracking system may remove or discardthe voxels associated with the non-moving objects by replacing theX-values, the Y-values, and/or the depth values with a zero value oranother suitable indicator or flag that may indicate the voxel may beinvalid.

At 320, one or more extremities such as one or more body parts may bedetermined for the isolated foreground object such as the human target.For example, in one embodiment, the target recognition, analysis, andtracking system may apply one or more heuristics or rules to theisolated human target to determine, for example, a centroid or center, ahead, shoulders, a torso, arms, legs, or the like associated with theisolated human target. According to one embodiment, based on thedetermination of the extremities, the target recognition, analysis, andtracking system may generate and/or adjust a model of the isolated humantarget. For example, if the depth image received at 305 may be includedin an initial frame observed or captured by a capture device such as thecapture device 20 described above with respect to FIGS. 1A-2, a modelmay be generated based on the location of the extremities such as thecentroid, head, shoulders, arms, hands, legs, or the like determined at320 by, for example, assigning a joint of the skeletal model to thedetermined locations of the extremities, which will be described in moredetail below. Alternatively, if the depth image may be included in asubsequent or non-initial frame observed or captured by the capturedevice, a model that may have been previously generated may be adjustedbased on the location of the extremities such as the centorid, head,shoulders, arms, hands, legs, or the like determined at 320, which willbe described in more detail below.

According to an example embodiment, upon isolating the foreground objectsuch as the human target at 315, the target recognition, analysis, andtracking system may calculate an average of the voxels in the humantarget to, for example, estimate a centroid or center of the humantarget at 320. For example, the target recognition, analysis, andtracking system may calculate an average position of the voxels includedin the human target that may provide an estimate of the centroid orcenter of the human target. In one embodiment, the target recognition,analysis, and tracking system may calculate the average position of thevoxels associated with the human target based on X-values, Y-values, anddepth values associated with the voxels. For example, as describedabove, the target recognition, analysis, and tracking system maycalculate an X-value for a voxel by averaging the X-values of the pixelsassociated with the voxel, a Y-value for the voxel by averaging theY-values of the pixels associated with the voxel, and a depth value forthe voxel by averaging the depth values of the pixels associated withthe voxel. At 320, the target recognition, analysis, and tracking systemmay average the X-values, the Y-values, and the depth values of thevoxels included in the human target to calculate the average positionthat may provide the estimate of the centroid or center of the humantarget.

FIG. 8 illustrates an example embodiment of a centroid or center beingestimated for a human target 402 b. According to an example embodiment,a location or position 802 of a centroid or center may be based on anaverage position or location of the voxels associated with the isolatedhuman target 402 b as described above.

Referring back to FIG. 5, the target recognition, analysis, and trackingsystem may then define a bounding box for the human target at 320 todetermine, for example, a core volume of the human target that mayinclude the head and/or torso of the human target. For example, upondetermining an estimate of the centroid or center of the human target,the target recognition, analysis, and tracking system may searchhorizontally along the X-direction to determine a width of the humantarget that may be used to define the bounding box associated with thecore volume. According to one embodiment, to search horizontally alongthe X-direction to measure the width of the human target, the targetrecognition, analysis, and tracking system may search in a leftdirection and a right direction along the X-axis from the centroid orcenter until the target recognition, analysis, and tracking system mayreach an invalid voxel such as a voxel that may not include a depthvalue associated therewith or a voxel that may be associated withanother object identified in the scene. For example, as described above,the voxels associated with the background may be removed to isolate thehuman target and the voxels associated therewith at 315. As describedabove, according to an example embodiment, to remove the voxels at 315,the target recognition, analysis, and target system may replace theX-values, the Y-values, and/or the depth values associated with thevoxels of the background objects with a zero value or another suitableindicator or flag that may indicate the voxel may be invalid. At 320,the target recognition, analysis, and tracking system may search in theleft direction from the centroid of the human target until reaching afirst invalid voxel at a left side of the human target and may search inthe right direction from the centroid of the human target until reachinga second invalid voxel at the right side of the human target. The targetrecognition, analysis, and tracking system may then calculate or measurethe length based on, for example, a difference between the X-values of afirst valid voxel adjacent to the first invalid voxel reached in theleft direction and a second valid voxel adjacent to the second invalidvoxel in the right direction.

The target recognition, analysis, and tracking system may then searchvertically along the Y-direction to determine a height of the humantarget from, for example, the head to the hips that may be used todefine the bounding box associated with the core volume. According toone embodiment, to search vertically along the Y-direction to measurethe width of the human target, the target recognition, analysis, andtracking system may search in a upward direction and a downwarddirection along the Y-axis from the centroid or center until the targetrecognition, analysis, and tracking system reaches an invalid voxel or avoxel that may not include an X- value, a Y-value, or a depth valueassociated therewith. For example, at 320, the target recognition,analysis, and tracking system may search in the upward direction fromthe centroid of the human target until reaching a third invalid voxel ata top portion of the human target and may search in the downwarddirection from the centroid of the human target until reaching a fourthinvalid voxel at a bottom portion of the human target. The targetrecognition, analysis, and tracking system may then calculate or measurethe height based on, for example, a difference between the Y-values of athird valid voxel adjacent to the third invalid voxel reached in theupward direction and a fourth valid voxel adjacent to the fourth invalidvoxel in the upward direction.

According to an example embodiment, the target recognition, analysis,and tracking system may further search diagonally along the X- andY-directions on the X- and Y-axis at various angles such as a 30 degree,a 45 degree angle, a 60 degree angle or the like to determine otherdistances and values that may be used to define the bounding boxassociated with the core volume.

Additionally, the target recognition, analysis, and tracking system maydefine the bounding box associated with the core volume based on ratiosof distances or values. For example, in one embodiment, the targetrecognition, analysis, and tracking system may define a width of thebounding box based on the height determined as described abovemultiplied by a constant variable such as 0.2, 0.25, 0.3 or any othersuitable value.

The target recognition, analysis, and tracking system may then define abounding box that may represent the core volume based on the first andsecond valid voxels determined by the horizontal search along theX-axis, the third and fourth valid voxels determined by the verticalsearch along the along the Y-axis, or other distances and valuesdetermined by, for example diagonal searches. For example, in oneembodiment, the target recognition, analysis, and tracking system maygenerate a first vertical line of the bounding box along the Y-axis atthe X-value of the first valid voxel and a second vertical line of thebounding box along the Y-axis at the X-value of the second valid voxel.Additionally, the target recognition, analysis, and tracking system maygenerate a first horizontal line of the bounding box along the X-axis atthe Y-value of the third valid voxel and a second horizontal line of thebounding box along the X-axis at the Y-value of the fourth valid voxel.According to an example embodiment, the first and second horizontallines may intersect the first and second vertical lines to form arectangular or square shape that may represent the bounding boxassociated with the core volume of the human target.

FIG. 9 illustrates an example embodiment of a bounding box 804 that maybe defined to determine a core volume. As shown in FIG. 9, the boundingbox 804 may form a rectangular shape based on the intersection of afirst vertical line VL1 and a second vertical line VL2 with a firsthorizontal line HL1 and a second horizontal line HL2 determined asdescribed above.

Referring back to FIG. 5, the target recognition, analysis, and trackingsystem may then determine a head of the human target at 320. Forexample, in one embodiment, after determining the core volume anddefining the bounding box associated therewith, the target recognition,analysis, and tracking system may determine a location or position ofthe head of the human target.

To determine the position or location of the head, the targetrecognition, analysis, and tracking system may search for variouscandidates at positions or locations suitable for the head, may scorethe various candidates, and may then select the position of head fromthe various candidates based on the scores. For example, according toone embodiment, the target recognition, analysis, and tracking systemmay search for an absolute highest voxel of the human target and/orvoxels adjacent to or near the absolute highest voxel, one or moreincremental voxels based on the location of the head determined for aprevious frame, a highest voxel on an upward vector that may extendvertically from, for example, the centroid or center and/or voxelsadjacent or near the highest voxel on a previous upward vectordetermined for a previous frame, a highest voxel on a previous upwardvector between a center and a highest voxel determined for a previousframe, or any other suitable voxels that may be a candidate for thehead.

The target recognition, analysis, and tracking system may then score thecandidates. According to one embodiment, the candidates may be scoredbased 3-D pattern matching. For example, the target recognition,analysis, and tracking system may create a head cylinder and a shouldercylinder. The target recognition, analysis, and tracking system may thencalculate a score for the candidates based on the number of voxelsassociated with the candidates that may included in the head cylinder,which will be described in more detail below.

FIG. 10 illustrates an example embodiment of a head cylinder 806 and ashoulder 808 that may be created to score candidates associated with thehead. According to an example embodiment, the target recognition,analysis, and tracking system may calculate a score for the candidatesbased on the number of voxels associated with the head candidatesincluded in the head cylinder 806 and the shoulder cylinder 808. Forexample, the target recognition, analysis, and tracking system maydetermine the total number head candidates inside the head cylinder 806and/or the shoulder cylinder 808 based on the location of the voxelsassociated with the head candidates and a total number of the headcandidates outside the head cylinder 806 (e.g, within an area 807)and/or the shoulder cylinder 808 based on the voxels associated with thehead candidates. The target recognition, analysis, and tracking systemmay further calculate a symmetric metric based on a function of anabsolute value of a difference between the number of the head candidatesin a left half LH of the shoulder cylinder 808 and the number of headcandidates in a right half RH of the shoulder cylinder 808. In anexample embodiment, the target recognition, analysis, and trackingsystem may then calculate the score for the candidates by subtractingthe total number of candidates outside the head cylinder 806 and/or theshoulder cylinder 808 from the total number of candidates inside thehead cylinder 806 and/or the shoulder cylinder 808 and furthersubtracting the symmetric metric from the difference between the totalnumber of candidates inside and outside the head cylinder 806 and/orshoulder cylinder 808. According to one embodiment, the target,recognition, analysis, and tracking system may multiple the total numberof candidates inside and outside the head cylinder 806 and/or theshoulder cylinder 808 by a constant determined by the targetrecognition, analysis, and tracking system before subtracting.

Referring back to FIG. 5, according to one embodiment, if a scoreassociated with one of the candidate exceeds a head threshold score, thetarget recognition, analysis, and tracking system may determine aposition or location of the head based on the voxels associated with thecandidate at 320. For example, in one embodiment, the targetrecognition, analysis, and tracking system may select a position orlocation of the head based on the highest point, the highest voxel on anupward vector that may extend vertically from, for example, the centroidor center and/or voxels adjacent or near the highest voxel on a previousupward vector determined for a previous frame, the highest voxel on aprevious upward vector between a center and a highest voxel determinedfor a previous frame, an average position of all the voxels within anarea such as a box, cube, or the like around a position or location ofthe head in a previous frame, or the like. According to other exampleembodiments, the target recognition, analysis, and tracking system maycalculate an average of the values such as the X-values, Y-values, anddepth values for the voxels associated with the candidate that mayexceed the head threshold score to determine the position or location ofthe head or the target recognition, analysis, and tracking system mayselect a position or location of the head based on a line fit or a lineof best fit of the voxels included in the candidate that may exceed thehead threshold score.

Additionally, in one embodiment, if more than one candidate exceeds thehead threshold score, the target recognition, analysis, and trackingsystem may select the candidate that may have the highest score and maythen determine the position or location of the head based on the voxelsassociated with the candidate that may have the highest score. Asdescribed above, the target, recognition, analysis, and tracking systemmay select a position or location of the head based on, for example, anaverage the values such as the X-values, Y-values, and depth values forthe voxels associated with the candidate that may have the highestscore.

According to one embodiment, if none of the scores associated with thecandidates exceeds the head threshold score, the target recognition,analysis, and tracking system may use a previous position or location ofthe head determined for voxels included in a human target associatedwith a depth image of a previous frame in which the head score may haveexceed the head threshold score or the target recognition, analysis, andtracking system may use a default position or location for a head in adefault pose of a human target such as a T-pose, a natural standing poseor the like, if the depth image received at 305 may be in an initialframe captured or observed by the capture device.

According to another embodiment, the target recognition, analysis, andtracking system may include one or more two-dimensional (2-D) patternsassociated with, for example, a head shape. The target recognition,analysis, and tracking system may then score the candidates associatedwith a head based on a likelihood that the voxels associated with thecandidate may be a head shape of the one or more 2-D patterns. Forexample, the target recognition, analysis, and tracking system maysample the depths values of adjacent or nearby voxels that may beindicative of defining a head shape. If a sampled depths value of one ofthe voxels that may be indicative of defining a head shape may deviatefrom one or more expected or predefined depth values of the voxels ofthe head shape, the target recognition, analysis, and tracking systemmay reduce a default score or an initial score to indicate that thevoxel may not be the head. In one embodiment, the target recognition,analysis, and tracking system may then select the score having thehighest value and may assign a location or position of the head based onthe location or position of the voxel associated with the candidatehaving the highest score.

According to another embodiment, the default score or the initial scoremay be the score for the candidates associated with the head calculatedusing the head and/or shoulder cylinder as described above. The targetrecognition, analysis, and tracking system may reduce such the score ifthe candidate may not be in a head shape associated with the one or morethe 2-D patterns. As described above, the target recognition, analysis,and tracking system may then select the score of the candidate thatexceeds a head threshold score and may assign a location or position ofthe head based on the location or position of the candidate.

The target recognition, analysis, and tracking system may furtherdetermine the shoulders and hips of the human target at 320. Forexample, in one embodiment, after determining the location or positionof the head of the human target, the target recognition, analysis, andtracking system may determine a location or a position of the shouldersand the of the human target. The target recognition, analysis, andtracking system may also determine an orientation of the shoulders andthe hips such as a rotation or angle of the shoulders and the hips.

According to an example embodiment, to determine a location or aposition of the shoulders and the hips, the target recognition,analysis, and tracking system may define a head-to-center vector basedon the location or position of the head and the centroid or center ofthe human target. For example, the head-to-center vector may be a vectoror line defined between the X-value, the Y-value, and the depth value(or Z-value) of the location or position of the head point and theX-value, the Y-value, and the depth value (or Z-value) of the locationor position of the centroid or center point.

FIG. 11 illustrates an example embodiment of a head-to-center vectorbased on a head and a centroid or center of a human target. As describedabove, a location or a position such as the location or position 810 ofthe head may be determined As shown in FIG. 11, the target recognition,analysis, and tracking system may then define a head-to-center vector812 between the location or position 810 of the head and the location orposition 802 of the center or centroid.

Referring back to FIG. 5, the target recognition, analysis, and trackingsystem may then define a shoulder volume box and a hips volume box basedon the head-to-center vector at 320. For example, according to oneembodiment, the target recognition, analysis, and tracking system maydefine or determine an approximate location or position of the shouldersand the hips based on a displacement such as a length from a bodylandmark such as the position or location associated with the head orthe centroid or center. The target recognition, analysis, and trackingsystem may then define the shoulder volume box and the hips volume boxaround the displacement value from the body landmark such as theposition or location associated with the head or the centroid or center.

FIG. 12 illustrates an example embodiment of a shoulders volume box SVBand a hips volume box HVB determined based on a head-to-center vector812. According to an example embodiment, the target recognition,analysis, and tracking system may define or determine an approximatelocation or position of the shoulders and the hips based on adisplacement such as a length from a body landmark such as the locationor position 810 associated with the head or the location or position 802associated with the centroid or center. The target recognition,analysis, and tracking system may then define the shoulder volume boxSVB and the hips volume box HVB around the displacement value from thebody landmark.

Referring back to FIG. 5, the target recognition, analysis, and trackingsystem may further calculate the center of the shoulders and the hipsbased on the displacement value such as the length from the bodylandmark such as the head along the head-to-center vector at 320. Forexample, the target recognition, analysis, and tracking system may movedown or up along the head-to-center vector by the displacement value tocalculate the center of the shoulders and the hips.

According to one embodiment, the target recognition, analysis, andtracking system may also determine an orientation such as an angle ofthe shoulders and the hips. In one embodiment, the target recognition,analysis, and tracking system may calculate a line fit of the depthvalues within, for example, the shoulders volume box and the hips volumebox to determine the orientation such as the angle of the shoulders andhips. For example the target recognition, analysis, and tracking systemmay calculate a line of best fit based on the X-values, Y-values, anddepth values of the voxels associated with the shoulders volume box andthe hips volume box to calculate a shoulders slope of a vector that maydefine a shoulders bone through the center of the shoulders and a hipsslope of a vector that may define a hips bone between joints of the hipsthrough the center of the hips. The shoulders slope and the hips slopemay define the respective orientation such as the angle of the shouldersand the hips.

According to another embodiment, the target recognition, analysis, andtracking system may mirror the depth values of the human target suchthat the depth values of the voxels may be reflected around the centerof the human target based on the head-to-center vector at 320. Thus, inone embodiment, the target recognition, analysis, and tracking systemmay compensate for the back of the body by reflecting the depth valuesof the voxels of the human target around a pivot vector such as thehead-to center-vector, a pivot point computed from the shoulders and/orthe hips bounding boxes, or the like.

The target recognition, analysis, and tracking system may then calculatethe line fit of the depth values including the reflected depth valueswithin, for example, the shoulders volume box and the hips volume box todetermine the orientation such as the angle of the shoulders and hips.For example, the target recognition, analysis, and tracking system maycalculate a line of best fit based on the X-values, Y-values, and depthvalues of the voxels including the reflected depth values associatedwith the shoulders volume box and the hips volume box to calculate ashoulders slope of a vector that may define a shoulders bone through thecenter of the shoulders and a hips slope of a vector that may define ahips bone between joints of the hips through the center of the hips. Theshoulders slope and the hips slope may define the respective orientationsuch as the angle of the shoulders and the hips.

FIG. 13 illustrates an example embodiment of shoulders and hips that maybe calculated based on the shoulders volume box SVB and the hips volumebox HVB. As shown in FIG. 13, a location or position 816 a-b of theshoulders and a location or position 818 a-b of the hips may bedetermined as described above based on the respective shoulders volumebox SVB and the hips volume box HVB.

Referring back to FIG. 5, at 320, the target recognition, analysis, andtracking system may then determine the torso of the human target. In oneembodiment, after determining the shoulders and the hips, the targetrecognition, analysis, and tracking system may generate or create atorso volume that may include the voxel associated with and surroundingthe head, the shoulders, the center, and the hips. The torso volume maybe a cylinder, a pill shape such as a cylinder with rounded ends, or thelike based on the location or position of the center, the head, theshoulders, and/or the hips.

According to one embodiment, the target recognition, analysis, andtracking system may create a cylinder that may represent the core volumehaving dimensions based on the shoulders, the head, the hips, thecenter, or the like. For example, the target recognition, analysis, andtracking system may create a cylinder that may have a width or adiameter based on the width of the shoulders and a height based on thedistance between the head and the hips. The target recognition,analysis, and tracking system may then orient or angle the cylinder thatmay represent the torso volume along the head-to-center vector such thatthe torso volume may reflect the orientation such as the angle of thetorso of the human target.

FIG. 14 illustrates an example embodiment of a cylinder 820 that mayrepresent the core volume. As shown in FIG. 14, the cylinder 820 mayhave a width or a diameter based on the width of the shoulders and aheight based on the distance between the head and the hips. The cylinder820 may also be oriented or angled along the head-to-center vector 812.

Referring back to FIG. 5, at 320, the target recognition, analysis, andtracking system may then estimate or determine the limbs of the humantarget. According to one embodiment, after generating or creating thetorso volume, the target recognition, analysis, and tracking system maycoarsely label voxels outside the torso volume as a limb. For example,the target recognition, analysis, and tracking system may identify eachof the voxels outside of the torso volume such that the targetrecognition, analysis, and tracking system may label the voxels as beingpart of a limb.

The target recognition, analysis, and tracking system may then determinethe actual limbs such as a right and left arm, a right and left hand, aright and left leg, a right and left foot, or the like associated withthe voxels outside of the torso volume. In one embodiment, to determinethe actual limbs, the target recognition, analysis, and tracking systemmay compare a previous position or location of an identified limb suchas the previous position or location of the right arm, left arm, leftleg, right leg, or the like with the position or location of the voxelsoutside of the torso volume. According to example embodiments, theprevious location or position of the previously identified limbs may bea location or position of a limb in a depth image received in a previousframe, a projected body part location or position based on a previousmovement, or any other suitable previous location or position of arepresentation of a human target such as a fully articulated skeleton orvolumetric model of the human target. Based on the comparison, thetarget recognition, analysis, and tracking system may then associate thevoxels outside of the torso volume with the closest previouslyidentified limbs. For example, the target recognition, analysis, andtracking system may compare the position or location including theX-value, Y-value, and depth value of each of the voxels outside of thetorso volume with the previous positions or locations including theX-values, Y-values, and depth values of the previously identified limbssuch as the previously identified left arm, right arm, left leg, rightleg, or the like. The target recognition, analysis, and tracking systemmay then associate each of the voxels outside the torso volume with thepreviously identified limb that may have the closest location orposition based on the comparison.

In one embodiment, to determine the actual limbs, the targetrecognition, analysis, and tracking system may compare a defaultposition or location of an identified limb such as the right arm, leftarm, right leg, left leg, or the like in a default pose of arepresentation of a human target with the position or location of thevoxels outside of the torso volume. For example, the depth imagereceived at 305 may be included in an initial frame captured or observedby the capture device. If the depth image received at 305 may beincluded in an initial frame, the target recognition, analysis, andtracking may compare a default position or location of a limb such asthe default position or location of a right arm, left arm, left leg,right leg, or the like with the position or location of the voxelsoutside of the torso volume. According to example embodiments, thedefault location or position of the identified limbs may be a locationor position of a limb in a default pose such as a T-pose, a Di Vincipose, a natural pose, or the like of a representation of a human targetsuch as a fully articulated skeleton or volumetric model of the humantarget in the default pose. Based on the comparison, the targetrecognition, analysis, and tracking system may then associate the voxelsoutside of the torso volume with the closest limb associated with thedefault pose. For example, the target recognition, analysis, andtracking system may compare the position or location including theX-value, Y-value, and depth value of each of the voxels outside of thetorso volume with the default positions or locations including theX-values, Y-values, and depth values of the default limbs such as thedefault left arm, right arm, left leg, right leg, or the like. Thetarget recognition, analysis, and tracking system may then associateeach of the voxels outside the torso volume with the default limb thatmay have the closest location or position based on the comparison.

The target recognition, analysis, and tracking system may also re-labelvoxels within the torso volume based on the estimated limbs. Forexample, in one embodiment, at least a portion of an arm such as a leftforearm may be positioned in front of the torso of the human target.Based on the previous position or location of the identified arm, thetarget recognition, analysis, and tracking system may determine orestimate the portion as being associated with the arm as describedabove. For example, the previous position or location of the previouslyidentified limb may indicate that the one or more voxels of a limb suchas an arm of the human target may be within the torso volume. The targetrecognition, analysis, and tracking system may then compare the previouspositions or locations including the X-values, Y-values, and depthvalues of the previously identified limbs such as the previouslyidentified left arm, right arm, left leg, right leg, or the like withthe position or location of voxels included in the torso volume. Thetarget recognition, analysis, and tracking system may then associate andrelabel each of the voxels inside the torso volume with the previouslyidentified limb that may have the closest location or position based onthe comparison.

According to one embodiment, after labeling the voxels associated withthe limbs, the target recognition, analysis, and tracking system maydetermine the location or position of, for example, portions of thelabeled limbs at 320. For example, after labeling the voxels associatedwith the left arm, the right arm, the left leg, and/or the right leg,the target recognition may determine the location or position of thehands and/or the elbows of the right and left arms, the knees and/or thefeet, the elbows, or the like.

The target recognition, analysis, and tracking system may determine thelocation or position of the portions such as the hands, elbows, feet,knees, or the like based on limb averages for each of the limbs. Forexample, the target recognition, analysis, and tracking system maycalculate a left arm average location by adding the X-values for each ofthe voxels of the associated with the left arm, the Y-values for each ofthe voxels associated with the left arm, and the depth values for eachof the voxels associated with the left arm and dividing the sum of eachof the X-values, Y-values, and depth values added together by the totalnumber of voxels associated with the left arm. According to oneembodiment, the target recognition, analysis, and tracking system maythen define a vector or a line between the left shoulder and the leftarm average location such that the vector or the line between the leftshoulder and the left arm average location may define a first searchdirection for the left hand. The target recognition, analysis, andtracking system may then search from the shoulders to along the firstsearch direction defined by the vector or the line for the last validvoxel or last voxel having a valid X-value, Y-value, and/or depth valueand may associate the location or position of the last valid voxel withthe left hand.

According to another embodiment, the target recognition, analysis, andtracking system may calculate an anchor point. The target recognition,analysis, and tracking system may then define a vector or a line betweenthe anchor point and the left arm average location such that the vectoror the line between the anchor point and the left arm average locationmay define a second search direction for the left hand. The targetrecognition, analysis, and tracking system may then search from theanchor point along the second search direction defined by the vector orthe line for the last valid voxel or last voxel having a valid X-value,Y-value, and/or depth value and may associate the location or positionof the last valid voxel with the left hand.

In an example embodiment, the target recognition, analysis, and trackingsystem may calculate the location or position of the anchor point basedon one or more offsets from other determined extremities such as thehead, hips, shoulders, or the like. For example, the target recognition,analysis, and tracking system may calculate the X-value and the depthvalue for the anchor point by extending the location or position of theshoulder in the respective X-direction and Z-direction by half of theX-value and depth value associated with the location or position of theshoulder. The target recognition, analysis, and tracking system may thenmirror the location or position of the X-value and the depth value forthe anchor point around the extended locations or positions.

The target recognition, analysis, and tracking system may calculate theY-value for the anchor point based on a displacement of the left armaverage location from the head and/or the hips. For example, the targetrecognition, analysis, and tracking system may calculate thedisplacement or the difference between the Y-value of the head and theY-value of the left arm average. The target recognition, analysis, andtracking system may then add the displacement or difference to theY-value of, for example, the center of the hips to calculate the Y-valueof the anchor point.

FIGS. 15A-15C illustrate example embodiments of a hand being determinedbased on anchor points 828 a-828 c. As shown in FIGS. 15A-15C, accordingto another embodiment, the target recognition, analysis, and trackingsystem may calculate anchor points 828 a-828 c. The target recognition,analysis, and tracking system may then define a vector or a line betweenthe anchor points 828 a-828 c and the left arm average locations 826a-826 c such that the vector or the line between the anchor point andthe left arm average location may define a second search direction forthe left hand. The target recognition, analysis, and tracking system maythen search from the anchor points 828 a-828 c along the second searchdirection defined by the vector or the line for the last valid voxel orlast voxel having a valid X-value, Y-value, and/or depth value and mayassociate the location or position of the last valid voxel with the lefthand.

As described above, in an example embodiment, the target recognition,analysis, and tracking system may calculate the location or position ofthe anchor points 828 a-828 c based on one or more offsets from otherdetermined extremities such as the head, hips, shoulders, or the like asdescribed above. For example, the target recognition, analysis, andtracking system may calculate the X-value and the depth value for theanchor points 828 a-828 c by extending the location or position of theshoulder in the respective X-direction and Z-direction by half of theX-value and depth value associated with the location or position of theshoulder. The target recognition, analysis, and tracking system may thenmirror the location or position of the X-value and the depth value forthe anchor points 828 a-828 c around the extended locations orpositions.

The target recognition, analysis, and tracking system may calculate theY-value for the anchor points 828 a-828 c based on a displacement of theleft arm average location from the head and/or the hips. For example,the target recognition, analysis, and tracking system may calculate thedisplacement or the difference between the Y-value of the head and theY-value of the left arm averages 826 a-826 c. The target recognition,analysis, and tracking system may then add the displacement ordifference to the Y-value of, for example, the center of the hips tocalculate the Y-value of the anchor point 828 a-828 c.

Referring back to FIG. 5, according to an example embodiment, the targetrecognition, analysis, and tracking system may calculate a right armaverage location that may be used to define a search direction such as afirst and second search direction as described above that may be used todetermine a location or position of a right hand at 320. The targetrecognition, analysis, and tracking system may further calculate a leftleg average location and a right leg average location that may be usedto define to a search direction as described above that may be used todetermine a left foot and a right foot.

FIG. 16 illustrates an example embodiment of hands and feet that may becalculated based on arm and leg average positions and/or anchor points.As shown in FIG. 16, a location or position 822 a-b of the hands and alocation or position 824 a-b of the feet that may be determined based onthe first and second search directions determined by the respective armand leg average positions and/or the anchor points as described above.

Referring back to FIG. 6, at 320, the target recognition, analysis, andtracking system may also determine a location or a position of elbowsand knees based on the right and left arm average locations and theright and the left leg average locations, the shoulders, the hips, thehead, or the like. In one embodiment, the target recognition, analysis,and tracking system may determine the location position of the leftelbow by refining the X-value, the Y-value, and the depth value of theleft arm average location. For example, the target recognition,analysis, and tracking system may determine the outermost voxels thatmay define edges associated with the left arm. The target recognition,analysis, and tracking system may then adjust X-value, the Y-value, andthe depth value of the left arm average location to be to be in themiddle or equidistance from the edges.

The target recognition, analysis, and tracking system may furtherdetermine additional points of interest for the isolated human target at320. For example, the target recognition, analysis, and tracking systemmay determine the farthest voxel away from the center of the body, theclosest voxel to the camera, the most forward voxel of the human targetbased on the orientation such as the angle of, for example, theshoulders.

The target recognition, analysis, and tracking system may then determinewhether one or more of the locations or positions determined for theextremities such as the head, the shoulders, the hips, the hands, thefeet, or the like may not have been accurate locations or positions forthe actual extremities of the human target at 320. For example, in oneembodiment, the location or position of the right hand may be inaccuratesuch that the location or position of the right hand may be stuck on oradjacent to the location or position of the shoulder or the hip.

According to an example embodiment, the target recognition, analysis,and tracking system may include or store a list of volume markers forthe various extremities that may indicate inaccurate locations orposition of the extremities. For example, the list may include volumemarkers around the shoulders and the hips that may be associated withthe hands. The target recognition, analysis, and tracking system maydetermine whether the location or position for the hands may be accuratebased on the volume markers associated with the hands in the list. Forexample, if the location or position of a hand may be within one of thevolume markers associated with the hand in the list, the targetrecognition, analysis, and tracking system may determine that thelocation or position of the hand may be inaccurate. According to oneembodiment, the target recognition, analysis, and tracking system maythen adjust the location or position of the hand to the previousaccurate location of the hand in a previous frame to the currentlocation or position of the hand.

At 325, the target recognition, analysis, and tracking system may tracka model that may be generated based on the extremities determined at320. For example, the target recognition, analysis, and tracking systemmay generate and/or may include a model such as a skeletal model thatmay have one or more joints and bones defined therebetween.

FIG. 17 illustrates an example embodiment a model 900 such as a skeletalmodel that may be generated. According to an example embodiment, themodel 900 may include one or more data structures that may represent,for example, a three-dimensional model of a human. Each body part may becharacterized as a mathematical vector having X, Y, and Z values thatmay define joints and bones of the model 900.

As shown in FIG. 17, the model 900 may include one or more jointsj1-j16. According to an example embodiment, each of the joints j1-j16may enable one or more body parts defined there between to move relativeto one or more other body parts. For example, a model representing ahuman target may include a plurality of rigid and/or deformable bodyparts that may be defined by one or more structural members such as“bones” with the joints j1-j16 located at the intersection of adjacentbones. The joints j1-16 may enable various body parts associated withthe bones and joints j1-j16 to move independently of each other. Forexample, the bone defined between the joints j10 and j12, shown in FIG.17, corresponds to a forearm that may be moved independent of, forexample, the bone defined between joints j14 and j16 that corresponds toa calf

Referring back to FIG. 5, at 325, the target recognition, analysis, andtracking system may adjust the generated model based on the location orpositions determined for the extremities at 320. For example, the targetrecognition, analysis, and tracking system may adjust the joint j1associated with the head to correspond the position or location such asthe location or position 810 for the head determined at 320. Thus, in anexample embodiment, the joint jl may be assigned the X-value, theY-value, and the depth value associated with the location or position810 determined for the head as described above. If one or more of theextremities may be inaccurate based on, for example, the list of volumemarkers described above, the target recognition, analysis, and trackingsystem may keep the inaccurate joints in their previous location orposition based on a previous frame.

Additionally, if the target recognition, analysis, and tracking systemmay not have locations or positions of the extremities, the targetrecognition, analysis, and tracking system may use a default location orposition based on a default pose such as a T-pose, Di Vinci pose, or thelike. For example, the target recognition, analysis, and tracking systemmay magnetize or adjust one or more of the joints j1-j16 of the model tobe associated the X-value, the Y-value, and/or the depth value of theclosest voxels in the default pose.

At 325, the target recognition, analysis, and tracking system may alsoadjust the measurements of one or more of the bones defined between thejoints of the model based on one or more body measurements determined at330, which will be described in more detail below. For example, thetarget recognition, analysis, and tracking system may determine thelength of the left forearm of the human target at 330. At 325, thetarget recognition, analysis, and tracking system may then adjust thelength of the bone associated with the left forearm to mimic the lengthof the left forearm determined for the human target at 330. For example,the target recognition, analysis, and tracking system may adjust one ormore of the X-values, Y-values, and depth values (or Z-values) of thejoints j10 and j12 such that the bone defined therebetween may be equalto the length determined for the left forearm of the human target at330.

At 325, the target recognition, analysis, and tracking system mayfurther check for invalid locations or positions of the jointsassociated with the adjusted model being. For example, in oneembodiment, the target recognition, analysis, and tracking system maycheck to determine whether a joint such as the joint j10 may be pokingout such that the model may be stuck in a chicken dance pose. Thus, at325, the target recognition, analysis, and tracking system may check themodel for known locations or positions where the model may collapse inan inappropriate manner such as the joints associated with the elbows.

According to one embodiment, the target recognition, analysis, andtracking system may further refine a location or position of a jointbased on X-values, Y-values, and depth values in the 2-D pixel area ofthe non-downsampled depth image received at 305. For example, in oneembodiment, the target recognition, analysis, and tracking system mayuse the data from the non-downsampled depth image to refine the locationor position of the joints of the model where, for example, the model maycollapse.

Additionally, the target recognition, analysis, and tracking system mayuse the data from the now-downsampled depth image to refine the locationor position of the joints of the model associated with frequently usedgestures. For example, according to one embodiment, the targetrecognition, analysis, and tracking system may prioritize the jointsassociated with the hands. The target recognition, analysis, andtracking system may localize the data around the hand in thenon-downsampled depth image received at 305 such that the targetrecognition, analysis, and tracking system may modify the location orposition of the hands determined at 320 using the higher resolution datain the non-downsampled depth image received at 305.

As described above, at 330, the target recognition, analysis, andtracking system may scan the voxels associated with the isolated humantarget to determine the dimensions of the extremities associatedtherewith. For example, the isolated human target may be scanned todetermine, for example, measurements such as lengths, widths, or thelike associated with the extremities such as the arms, legs, head,shoulders, hips, torso, or the like.

To determine the dimensions, at 330, the target recognition, analysis,and tracking system may generate an estimator of proposed jointdimensions for each of the extremities. The target recognition,analysis, and tracking system may calculate a mean and a standarddeviation for each of the proposed joint dimensions using theestimators. The target recognition, analysis, and tracking system mayadd the proposed joint dimensions within a defined percentage deviationand the outliers or the proposed joint dimensions outside the definedpercentage deviation may be rejected. The target recognition, analysis,and tracking system may then determine the dimensions of the extremitiesbased on the estimator that may have a highest ratio between thestandard deviation thereof and the number of the proposed jointdimensions.

The dimensions associated with the extremities determined by the scanmay also be updated at 330. According to one embodiment, the targetrecognition, analysis, and tracking system may include one or moreheuristics or rules to determine whether the dimensions determined bythe scan may be correct. For example, the target recognition, analysis,and tracking system may include a heuristic or rule that may determinewhether the Euclidean distance between symmetrical joints may be roughlyequivalent, a heuristic or rule that may determine whether the handsand/or elbows near the body, a heuristic and/or rule that may determinewhether the head may be locked in a position or location, a heuristicand/or rule that may determine whether the hands close to the head, orthe like that may be used to adjust the dimensions. As described above,the dimensions determined at 330 may be used to adjust the model thatmay be tracked for a subsequent frame at 325.

At 335, the adjusted model may be processed. For example, in oneembodiment, the target recognition, analysis, and tracking system mayprocess the adjusted model by, for example, mapping one or more motionsor movements applied to the adjusted model to an avatar or gamecharacter such that the avatar or game character may be animated tomimic the user such as the user 18 described above with respect to FIGS.1A and 1B. For example, the visual appearance of an on-screen charactermay then be changed in response to changes to the model being adjusted.

In one embodiment, the adjusted model may process the adjusted model byproviding the adjusted model to a gestures library in a computingenvironment such as the computing environment 12 described above withrespect to FIGS. 1A-4. The gestures library may be used to determinecontrols to perform within an application based on positions of variousbody parts in the skeletal model.

It should be understood that the configurations and/or approachesdescribed herein are exemplary in nature, and that these specificembodiments or examples are not to be considered limiting. The specificroutines or methods described herein may represent one or more of anynumber of processing strategies. As such, various acts illustrated maybe performed in the sequence illustrated, in other sequences, inparallel, or the like. Likewise, the order of the above-describedprocesses may be changed.

The subject matter of the present disclosure includes all novel andnonobvious combinations and subcombinations of the various processes,systems and configurations, and other features, functions, acts, and/orproperties disclosed herein, as well as any and all equivalents thereof

What is claimed:
 1. A method for tracking a user, the method comprising:receiving a depth image; generating a first voxel of a plurality ofvoxels based on at least one pixel of the depth image that correspondsto a background independent of at least one pixel that corresponds to ahuman target, the first voxel overlapping another voxel in the pluralityof voxels; removing the first voxel from the plurality of voxels toisolate one or more voxels associated with the human target; determininga location or position of one or more extremities of the isolated humantarget based on the one or more voxels associated with the human target;and adjusting a model based on the location or position of the one ormore extremities.